Extramural Lab Speaker Program
We have a vibrant program in which colleagues have shared their work with us. Below are some of the people who have given talks in our lab meeting.
Erfan Nozari received his B.Sc. degree in Electrical Engineering-Control in 2013 from Isfahan University of Technology, Iran, received his Ph.D. in Mechanical Engineering and Cognitive Science in 2019 from University of California San Diego, and was subsequently a postdoctoral researcher in the University of Pennsylvania Department of Electrical and Systems Engineering. He is currently an assistant professor at the University of Riverside, California Department of Mechanical Engineering. He has been the (co)recipient of the 2019 IEEE Transactions on Control of Network Systems Outstanding Paper Award, the Best Student Paper Award from the 57th IEEE Conference on Decision and Control, the Best Student Paper Award from the 2018 American Control Conference, and the Mechanical and Aerospace Engineering Distinguished Fellowship Award from the University of California San Diego. His research interests include systems and control theory and machine learning and their applications in computational and theoretical neuroscience.
Dr. Brielle Ferguson is a postdoctoral fellow at Stanford University. Her research is focused on discovering and better understanding circuits in the brain that support cognition and identifying pathways for intervention in psychiatric diseases and neurological disorders with cognitive impairments. Currently she is studying the mechanisms of attention impairments in a genetic mouse model of absence epilepsy. Outside of lab she is a fierce advocate for better diversity, equity, and inclusion in STEM. She recently co-founded and serves as program director for Black in Neuro, an organization that strives to build community, provide resources, and increase visibility for Black scholars in neuroscience-related fields.
Working memory intersects multiple cognitive processes, including attention, prospection, and bridges with long-term memory representations to guide complex behavior. Katie Hsiao’s current research explores the genetic and physiological mechanisms of working memory – how persistent neural activities in “executive” regions in the PFC, hippocampus, and thalamus, orchestrate future goal-directed behavior and allow individuals to act beyond the confines of the here and now. The work has critical implications for understanding everyday cognitive challenges, as well as ADHD, learning disability, and other neuropsychiatric disorders.
Katie earned her Master of Chemical Engineering degree from Rochester University. She did her Ph.D. in Neuroscience training at Icahn School of Medicine at Mount Sinai Hospital, and then joined Rajasethupathy Lab in 2017. Katie is the recipient of the Women & Science Fellowship 2018. In 2019, she received the Robertson Therapeutic Development Fund, which supports the translation of her research discoveries into innovations for the clinic that will serve the public good.
Julian Kosciessa received degrees from Freie Universität Berlin (B.Sc. Psychology, 2014) and Humboldt University of Berlin (M.Sc. Mind & Brain, 2016). Pursuing his interests in computational cognitive neuroscience, he became a IMPRS Comp2Psych (Computational Methods in Psychiatry and Ageing Research) fellow and received his Ph.D. in 2020 from Humboldt Universität zu Berlin. He is currently a postdoctoral fellow in the Lifespan Neural Dynamics Group at the Max Planck Institute for Human Development.
He studies how the human brain adapts perception, cognition and action to varying contextual demands. In particular, he focusses on the potential importance of different (thalamo-)cortical dynamics, such as rhythmic synchrony and irregularity, for such flexibility. In his previous research, he extended methods for a time-varying characterization of these dynamic states, and highlighted thalamic relations to cortical state shifts in service of flexible perception under uncertainty. Inspired by systems and cognitive neuroscience, he combines multiple measurement techniques in humans, including computational modeling of behavior, electroencephalography (EEG), structural and functional magnetic resonance imaging (s/fMRI) and pupillometry.
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